Method for person re-identification in closed place, system, and terminal device

ABSTRACT

The present disclosure provides a method, system, and terminal device for re-identifying a person in a closed place, relating to the field of video processing technologies. The method comprises: dividing a closed place into a plurality of regions comprising an entrance region, any of the regions comprising at least one camera for photographing; confirming a person first entering based on a trajectory tracking of the person in the entrance area, and assigning a unique identity number to the person, acquiring and recording feature information thereof and binding the identity number with the feature information; and when re-identification of a person in the closed place is performed, matching person&#39;s feature information captured by the camera of the region where the person is, with recorded feature information selecting the identity number bound with recorded feature information that best matches the feature information of the person as the identity number of the person.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based upon International Application No.PCT/CN 2018/106143, filed on Sep. 18, 2018, which is based upon andclaims priority of Chinese patent application No. 201710996027.4, filedon Oct. 23, 2017, the contents of which are hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of video processing, and inparticular, to a method, a system, and a terminal device forre-identifying a human being in a closed place.

BACKGROUND

The identification technology of the person's identity, especially there-identification technology of the person's identity in a closed publicplace, can be used for smart settlement, security, passenger flowanalysis, etc. in shopping malls, schools, hospitals, subways, etc.

There are mainly two existing manners for re-identifying a person'sidentity: one is based on trajectory tracking of the person, and theother is based on features of the person (such as features of clothingcolor, stature, etc.). The above two manners are both under developmentand have not yet reached maturity.

There are still many disadvantages for real-time identification of theperson's identity in the field of video surveillance, as follows.

a. When a monitoring area is relatively narrow, there are dead angles ona screen of a single camera, and there are situations such as occlusion,crossover, etc. between the persons, and the persons may enter into andleave the place randomly. It is difficult for the existing persontrajectory tracking technology to correctly track trajectories ofmultiple persons in the place.b. For features such as the person's dress and posture, it is difficultto distinguish them with extracted features of the existing neuralnetwork. Therefore, purely based on features of the person foridentification of the identity, the accuracy is poor.c. The efficiency of the existing technologies is also difficult toachieve real-time processing.

Therefore, a novel method for re-identifying a person's identity isneeded.

The information disclosed in the Background section above is only forenhancing the understanding of the background of the present disclosure,and thus may include information that does not constitute prior artknown to those of ordinary skill in the art.

SUMMARY

The purpose of the present disclosure is to provide a method, system andterminal device for re-identifying a human being in a closed place, soas to overcome one or more problems caused by the limitations anddefects of the related art at least to some extent.

Other features and advantages of the present disclosure will becomeapparent from the following detailed description, or may be learned inpart through the practice of the present disclosure.

According to a first aspect of the present disclosure, a method forre-identifying a human being in a closed place is disclosed, including:

dividing the closed place into multiple areas comprising an entrancearea, and having at least one camera for shooting in any area;

confirming a human being first entering based on a trajectory trackingof the human being in the entrance area, assigning a unique identitynumber to the human being, acquiring and recording the featureinformation of the human being, and binding the identity number with thefeature information of the human being; and

when re-identification of a human being in the closed place isperformed, matching the feature information of the human being capturedby the camera in an area where the human being is with the recordedfeature information, and selecting the identity number bound with therecorded feature information that best matches the feature informationof the human being as the identity number of the human being.

According to an exemplary embodiment of the present disclosure, themethod further includes: when the human being leaves, deleting thefeature information of the human being and marking the human being ashaving left.

According to an example implementation of the present disclosure, havingat least one camera for shooting in any area includes:

having two cameras with different shooting angles for shooting in anyarea.

According to an exemplary embodiment of the present disclosure,confirming a human being first entering based on a trajectory trackingof the human being in the entrance area includes: judging whether thehuman being is the human being first entering according to a trajectoryof the human being displayed on continuous shot screens with multipleframes of the entrance area.

According to an exemplary embodiment of the present disclosure,acquiring feature information of the human being is performed by aconvolutional neural network technology.

According to an exemplary embodiment of the present disclosure, thefeature information of the human being includes: stature, dress, and/orappearance.

According to an exemplary embodiment of the present disclosure, matchingthe feature information of the human being captured by the camera in anarea where the human being is with the recorded feature informationincludes: performing matching on all human beings in the area.

According to an exemplary embodiment of the present disclosure, thefeature information and the identity number are stored in a human beingfeature database.

According to a second aspect of the present disclosure, a system forre-identifying a human being in a closed place is disclosed, including:

a monitoring module, configured to divide the closed place into multipleareas comprising an entrance area, and have at least one camera forshooting in any area;

a first entered human being confirmation module, configured to confirm ahuman being first entering based on a trajectory tracking of the humanbeing in the entrance area, assign a unique identity number to the humanbeing, acquire and record the feature information of the human being,and bind the identity number with the feature information of the humanbeing; and

a re-identification module, configured to: when re-identification of ahuman being in the closed place is performed, match the featureinformation of the human being captured by the camera in an area wherethe human being is with recorded feature information, and select theidentity number bound with the recorded feature information that bestmatches the feature information of the human being as the identitynumber of the human being.

According to an exemplary embodiment of the present disclosure, thesystem further includes a feature database, configured to store thefeature information and the identity number.

According to a third aspect of the present disclosure, acomputer-readable storage medium having a computer program storedtherein is disclosed, wherein the program, when executed by a processor,causes the implementation of following steps of the method:

dividing the closed place into multiple areas comprising an entrancearea, and having at least one camera for shooting in any area;

confirming a human being first entering based on a trajectory trackingof the human being in the entrance area, assigning a unique identitynumber to the human being, acquiring and recording the featureinformation of the human being, and binding the identity number with thefeature information of the human being; and

when re-identification of a human being in the closed place isperformed, matching the feature information of the human being capturedby the camera in an area where the human being is with the recordedfeature information, and selecting the identity number bound with therecorded feature information that best matches the feature informationof the human being as the identity number of the human being.

According to a fourth aspect of the present disclosure, a terminaldevice is disclosed, including:

a processor; and

a memory storing instructions for the processor to control the followingoperations:

dividing the closed place into multiple areas comprising an entrancearea, and having at least one camera for shooting in any area;

confirming a human being first entering based on a trajectory trackingof the human being in the entrance area, assigning a unique identitynumber to the human being, acquiring and recording the featureinformation of the human being, and binding the identity number with thefeature information of the human being; and

when re-identification of a human being in the closed place isperformed, matching the feature information of the human being capturedby the camera in an area where the human being is with the recordedfeature information, and selecting the identity number bound with therecorded feature information that best matches the feature informationof the human being as the identity number of the human being.

According to a fifth aspect of the present disclosure, a system forre-identifying a human being in a closed place is disclosed, including:

a processor; and

a memory storing instructions for the processor to control the followingoperations:

dividing the closed place into multiple areas including an entrancearea, and having at least one camera for shooting in any area;

confirming a human being first entering based on a trajectory trackingof the human being in the entrance area, assigning a unique identitynumber to the human being, acquiring and recording the featureinformation of the human being, and binding the identity number with thefeature information of the human being; and

when re-identification of a human being in the closed place isperformed, matching the feature information of the human being capturedby the camera in an area where the human being is with the recordedfeature information, and selecting the identity number bound with therecorded feature information that best matches the feature informationof the human being as the identity number of the human being.

According to an exemplary embodiment of the present disclosure, theprocessor is further configured to delete the feature information of thehuman being and marking the human being as having left, when the humanbeing leaves.

According to an exemplary embodiment of the present disclosure, havingat least one camera for shooting in any area includes: having twocameras with different shooting angles for shooting in any area.

According to an exemplary embodiment of the present disclosure,confirming a human being first entering based on a trajectory trackingof the human being in the entrance area includes: judging whether thehuman being is the human being first entering according to a trajectoryof the human being displayed on continuous shot screens with multipleframes of the entrance area.

According to an exemplary embodiment of the present disclosure,acquiring feature information of the human being is performed by aconvolutional neural network technology.

According to an exemplary embodiment of the present disclosure, thefeature information of the human being includes: stature, dress, and/orappearance.

According to an exemplary embodiment of the present disclosure, matchingthe feature information of the human being captured by the camera in anarea where the human being is with the recorded feature informationincludes: performing matching on all human beings in the area.

According to an exemplary embodiment of the present disclosure, thefeature information and the identity number are stored in a human beingfeature database.

According to some exemplary embodiments of the present disclosure, byadopting a solution with combination of the multi-camera-based, thetrajectory tracking and feature matching, the problem of the human beingre-identification in real-time and in the complex scene is solved,achieving a better effect of the human being re-identification.

According to some exemplary embodiments of the present disclosure, bydisposing multiple cameras in the place to cover different areas anddifferent angles, any area can be shot by two or more cameras fromdifferent angles, which can solve the problem of occlusion between thehuman beings.

According to some exemplary embodiments of the present disclosure, byperforming association matching on all human beings in the entirescreen, associating and matching all human beings with the identitiescan help find global optimal matching, which improves the tolerance offeature comparison greatly compared with the human beingre-identification in a single detection box.

It should be noted that the above general description and the followingdetailed description are merely exemplary and explanatory and should notbe construed as limiting of the disclosure,

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features, and advantages of the presentdisclosure will become more apparent by describing its exemplaryembodiments in detail with reference to the accompanying drawings.

FIG. 1 shows a flowchart of a method for re-identifying a human being ina closed place according to an exemplary embodiment of the presentdisclosure.

FIG. 2 shows a real shot picture of an entrance area in a closed place.

FIG. 3 shows a real shot picture of a non-entrance area in a closedplace.

FIG. 4 shows another real shot picture of an entrance area in a closedplace.

FIG. 5 shows a flowchart of a method for re-identifying a human being ina closed place according to another exemplary embodiment of the presentdisclosure.

FIG. 6 shows a block diagram of a system for re-identifying a humanbeing in a closed place according to an exemplary embodiment of thepresent disclosure.

FIG. 7 shows a terminal device according to an exemplary embodiment ofthe present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments will now be described more fully with reference tothe accompanying drawings. However, the exemplary embodiments can beimplemented in a variety of forms and should not be construed as beinglimited to the examples set forth herein; rather, these exemplaryembodiments are provided so that this disclosure will be morecomprehensive and complete, so as to convey the idea of the exemplaryembodiments to those skilled in this art. The drawings are merelyschematic representations of the present disclosure and are notnecessarily drawn to scale. The same reference numerals in the drawingsdenote the same or similar parts, and the detailed description thereofwill be omitted.

In addition, the features, structures, or features described above maybe combined in any suitable manner in one or more embodiments. In thedescription below, numerous specific details are set forth to provide athorough understanding of the exemplary embodiments of the presentdisclosure. However, those skilled in the art will appreciate that thetechnical solution of the present disclosure may be practiced withoutone or more of the specific details, or other methods, components,steps, and the like may be employed, in other instances, well-knownstructures, methods, implementations, or operations are not shown ordescribed in detail to avoid obscuring various aspects of the presentdisclosure.

Some of the block diagrams shown in the figures are functional entitiesand do not necessarily correspond to physically or logically separateentities. These functional entities may be implemented in software, orimplemented in one or more hardware modules or integrated circuits, orimplemented in different networks and/or processor devices and/ormicrocontroller devices.

An object of the present disclosure is to provide a method, a system,and a terminal device for re-identifying a human being in a closedplace. The method for re-identifying a human being in a closed placeincludes: dividing the closed place into multiple areas including anentrance area, and having at least one camera for shooting in any areaconfirming a human being first entering based on a trajectory trackingof the human being in the entrance area, assigning a unique identitynumber to the human being, acquiring and recording the featureinformation of the human being, and binding the feature information withan identity number; and when re-identification of a human being in theclosed place is performed, matching the feature information of the humanbeing captured by the camera in an area where the human being is withthe recorded feature information, and selecting the identity numberbound with the recorded feature information that best matches thefeature information of the human being as the identity number of thehuman being. By adopting a solution with combination of the trajectorytracking and feature matching, the problem of the human beingre-identification in real-time and in the complex scene is solved,achieving a better effect of the human being re-identification.Meanwhile, by disposing multiple cameras in the place to cover differentareas and different angles, any area can be shot by two or more camerasfrom different angles, which can solve the problem of occlusion betweenthe human beings. In addition, by performing association matching on allhuman beings in the entire screen, associating and matching all humanbeings with the identities can help find global optimal matching, whichimproves the tolerance of the feature comparison greatly compared withthe human being re-identification in a single detection box.

The method, system, and terminal device for re-identifying a human beingin a closed place according to the present disclosure will be describedin detail below with reference to FIGS. 1-7. FIG. 1 shows a flowchart ofa method for re-identifying a human being in a closed place according toan exemplary embodiment of the present disclosure; FIG. 2 shows a realshot picture of an entrance area in a closed place; FIG. 3 shows a realshot picture of a non-entrance area in a closed place; FIG. 4 showsanother real shot picture of an entrance area in a closed place; FIG. 5shows a flowchart of a method for re-identifying a human being in aclosed place according to another exemplary embodiment of the presentdisclosure; FIG. 6 shows a block diagram of a system for re-identifyinga human being in a closed place according to an exemplary embodiment ofthe present disclosure; FIG. 7 shows a terminal device according to anexemplary embodiment of the present disclosure.

It should be particularly noted herein that the exemplary embodiment ofthe present disclosure uses the human being re-identification in anunmanned convenience store as an example for description, but thepresent disclosure is not limited thereto. The method, system, andterminal device for re-recognizing a human being in a closed place ofthe present disclosure can also apply to smart settlement, security,passenger flow analysis, etc. in shopping malls, schools, hospitals,subways, etc.

First, a method for re-identifying a human being in a closed placeaccording to the present disclosure will be described in detail withreference to FIGS. 1-5. FIG. 1 shows a flowchart of a method forre-identifying a human being in a closed place according to an exemplaryembodiment of the present disclosure; FIG. 2 shows a real shot pictureof an entrance area in a closed place; FIG. 3 shows a real shot pictureof a non-entrance area in a closed place; FIG. 4 shows another real shotpicture of an entrance area in a closed place; FIG. 5 shows a flowchartof a method for re-identifying a human being in a closed place accordingto another exemplary embodiment of the present disclosure.

Before the embodiments are described in detail, a model to re-identifythe human being in the closed place used by the present disclosure isintroduced briefly: when a human being enters the closed place, thesystem notifies the algorithm there is a human being first entering. Thehuman being first entering is bound based on the human being trackingand the feature information of the human being first entering iscollected in the present disclosure. When the human being leaves, thesystem notifies the human being has left, and the feature information ofthe human being is deleted, and the human being is marked as havingleft. When the human being is in the place, the identity information ofthe human being at any position can be given at any time. For example,at 9:30 on Oct. 10, 2017, the human being appearing in a certain area ata certain location is Tonny (just an assumed name, the identity numbercan also be used instead of the name, i.e., naming them directly as 1,2, . . . n).

FIG. 1 shows a flowchart of a method for re-identifying a human being ina closed place according to an exemplary embodiment of the presentdisclosure.

In S102, the closed place is divided into multiple areas including anentrance area, and there is at least one camera used for shooting in anyarea.

Since the above closed place may be relatively narrow, there are deadangles on a screen of the single camera, and there are situations suchas overlapping, occlusion, etc. between the human beings, themulti-camera arrangement is usually used. Multiple cameras are disposedin the place to cover different angles and different areas. Theprinciple is that any area can be shot by two or more cameras fromdifferent angles, which can solve the problem of occlusion between thehuman beings.

In addition to eliminating the dead corners in the place, each of themultiple camera has a respective function, as described below

a. A main camera monitors the entrance area to monitor the human beingfirst entering, and tracks the human being to acquire features of thehuman being (such as front, side, back clothing, physical features). Thereason why the entrance area is selected to collect features is thatthis area is relatively wide, and there is less interference such asocclusion and overlapping between the human beings so as not to confusethe features due to tracking errors.

As shown in FIG. 2, the larger white box is the range of the trackingarea, the smaller white box is the position of the entrance (roughlydrawn). According to the trajectory analysis of the human being in thearea, the human being who newly enters the entrance can be monitored andthe feature information of the human being can be acquired.

b. Other cameras are configured to re-identify the human being.Depending on the angle of the respective camera, each area of the placeis designated one responsible camera. When the identity of a human beingin a certain area needs to be identified, a designated responsiblecamera corresponding to the area can be found, and a screen at thecorresponding moment is retrieved, as shown in FIG. 3.

It should be particularly noted herein that the number of the camerasfor monitoring the entrance area may be one or multiple, and the presentdisclosure is not limited thereto. For example, only one camera may beused to both monitor the human being first entering and re-identify thehuman being in the entrance area; or one camera is used to monitor thehuman being first entering, and at the same time, another camera ormultiple cameras with different shooting angles are used to re-identifythe human being in the entrance area, in order to improve the efficiencyand real-time processing.

In S104, a human being first entering is confirmed based on a trajectorytracking of a human being in the entrance area, a unique identity numberis assigned to the human being first entering, feature information ofthe human being first entering is acquired and recorded, and theidentity number is bound with the feature information of the humanbeing.

The existing human being tracking technology can be divided intoreal-time tracking and multi-frame global tracking according to time.The former directly predicts the position of the human being in the nextframe based on the position and feature information of the human beingat the previous time, which can be used for real-time videosurveillance. The latter is to “detect+associate” the human beings inall frames of the captured video, and can only be used for postmortemvideo analysis.

Considering from the perspective of performance, multi-frame globaltracking can make full use of multi-frame information, achieving abetter effect in solving occlusion between the human being s, and thehuman being disappearing from the screen for a long time or a shorttime, and having a stronger anti-interference ability. The disadvantageis that the tracking results cannot be given in real time. The problemto be solved by the tracking of the present disclosure is the binding ofthe human being first entering with the identity, and the collection ofthe feature information, so there is no need to give the tracking pathin real time, and the present disclosure adopts the multi-frame globaltracking scheme.

The specific method is to judge whether the human being is the humanbeing first entering according to a trajectory of the human beingdisplayed on continuous shot screens with multiple frames of theentrance area. For example, when the trajectory of the human being showsa direction extending from the outside of the entrance to the inside ofthe entrance (Of course, other auxiliary judging conditions can also beadded), it can be determined that the human being is a human being firstentering and can be assigned a unique identity number. After confirmingthat there is the human being first entering, the present disclosureenables the detection and tracking for a predetermined duration, forexample, 5 seconds (which can be selected in the range of 3-10 secondsbased on needs) for the entrance area, and the feature information ofthe human being first entering can be collected on the tracking path.After 5 seconds, the feature information of the human being firstentering is confirmed through calculation, and the unique identitynumber is assigned to the human being first entering for binding. Thenthe feature information and the corresponding identity number arerecorded.

The present disclosure collects convolutional neural network (CNN)features of the human being in the detection boxes of these consecutiveframes as the feature information. Specifically, “detection” is thehuman being detection based on the convolutional neural network (CNN).“Feature information” is a collection of features of various aspectssuch as posture, dress and stature of the human being extracted by theconvolutional neural network (CNN) technology. Training is performedbased on the disclosed CNN network for the human being detection and thefeature extraction in combination with collected and labeledmulti-camera human being tracking data, to perform pedestrian detection,and improve the human being feature network.

According to an exemplary embodiment of the present disclosure, thefeature information of the human being includes: stature, dress, and/orappearance. The stature may include height and weight of a human being,and the dress may include clothes type and color of a human being.

According to an exemplary embodiment of the present disclosure, thefeature information and the identity number are stored in a human beingfeature database.

In S106, when re-identification of the human being in the closed placeis performed, feature information of the human being captured by thecamera in an area where the human being is located is matched withrecorded feature information, and the identity number bound with therecorded feature information that best matches the feature informationof the human being is selected as the identity number of the humanbeing.

Since the feature information is collected for each human being firstentering in S104, the identity of the human being framed by any humanbeing detection box can be identified based on the comparison of thefeature information. When it is necessary to re-identify a human beingin a certain area of the closed place, the feature information of thehuman being acquired through the designated responsible camera in thearea where the human being is located is matched with the recordedfeature information, and the identity number bound with the recordedfeature information that best matches the feature information of thehuman being is selected as the identity number of the human being. Forexample, as shown in FIG. 1 when someone wants to know the identity ofthe man who picks up the goods on the smart shelf in the lower rightcorner (standing at the white box), the feature information of the manacquired through the designated responsible camera in the area where theman is located is matched with the recorded feature information, and theidentity number bound with the most matched feature information is usedas the identity number of the man, and the identity of the man isdetermined to be the customer XXX (or XX number). Similarly, acquiringthe feature information of the man is also performed by theconvolutional neural network technology.

Further, considering that the distinction of feature information betweenbeings may be not clear (for example, it is not easy to distinguish twohuman beings wearing black clothes), the present disclosure performsassociation matching on all human beings in the entire screen. Thisassociating and matching all human beings with the identities can helpfind global optimal matching, which improves the tolerance of thefeature information comparison greatly compared with the human beingre-identification in a single detection box. This process enablesreal-time processing.

A specific example is shown in FIG. 4. In any frame that needs there-identification, the collected feature information is used to performoptimal association matching for all human beings in the area. That isto say, although we only want to re-identify a certain human being (suchas a lady wearing the black clothes on the rightmost side of the screenin FIG. 4), matching is performed on all human beings in the area wherethe human being (the lady) is located, which can greatly improve thetolerance of feature comparison. That is, because the lady in black isnot easy to distinguish from other human beings in black or dark clothes(that is, there are more than one identity number corresponding to thefeature information that the color of the clothes is black), which mayresult in a matching error, the matching is performed on all the humanbeings. Others in black or dark clothes may have other types of clearlydistinguishing features that can quickly and clearly identify them, thusthe identities (numbers) of others in black or dark clothes can bequickly confirmed by the clearly distinguishing features, and thesehuman beings (or the identity numbers) can be excluded. As such, thereis only one identity number corresponding to the feature informationthat the color of the clothes is black. This identity number is theidentity number of the lady.

According to an exemplary embodiment of the present disclosure, when ahuman being leaves, the feature information of the human being isdeleted and the human being is marked as having left, as shown in S508in FIG. 5. S502-S506 are the same as S102-S106, which will not repeatedhere.

In S508, when the camera that monitors the area of the entrance (aclosed place shared by the entrance and exit) or the exit (a closedplace with separated entrance and exit) detects a human being leaving,the feature information of the human being is deleted and the humanbeing is marked as having left, and the bound identity number of thehuman being is released.

FIG. 6 shows a block diagram of a system for re-identifying a humanbeing in a closed place according to an exemplary embodiment of thepresent disclosure.

As shown in FIG. 6, the system 600 for re-identifying a human being in aclosed place may include a monitoring module 602, a first entered humanbeing confirmation module 604, and a re-identification module 606.

The monitoring module 602 is configured to divide the closed place intomultiple areas including an entrance area, and have at least one camerafor shooting in any area. The first entered human being confirmationmodule 604 is configured to confirm a human being first entering basedon a trajectory tracking of a human being in the entrance area, assign aunique identity number to the human being, acquire and record thefeature information of the human being, and bind the identity numberwith the feature information of the human being. The re-identificationmodule 606 is configured to: when re-identification of the human beingin the closed place is required, match feature information of the humanbeing captured by the camera in an area where the human being is withrecorded feature information, and select the identity number bound withthe recorded feature information that best matches the featureinformation of the human being as the identity number of the humanbeing.

In addition, the system 600 may further include a feature database 608,configured to store the feature information and the identity number.

As another aspect, the present disclosure also provides acomputer-readable medium, which may be included in the system describedin the above embodiments; or may exist alone without being assembledinto the system. The computer-readable medium described above carriesone or more programs, and the one or more programs, when executed by onesystem, causes the system to execute: dividing the closed place intomultiple areas including an entrance area, and having at least onecamera for shooting in any area; confirming a human being first enteringbased on a trajectory tracking of a human being in the entrance area,assigning a unique identity number to the human being, acquiring andrecording the feature information of the human being, and binding thefeature information with an identity number; and when re-identificationof the human being in the closed place is required, matching the featureinformation of the human being captured by the camera in an area wherethe human being is with the recorded feature information, and selectingthe identity number bound with the recorded feature information thatbest matches the feature information of the human being as the identitynumber of the human being.

FIG. 7 shows a terminal device according to an exemplary embodiment ofthe present disclosure.

As shown in FIG. 7, the terminal device 700 may include a processor 710and a memory 720. In addition, according to an embodiment, the terminaldevice may further include a transmitter and a receiver.

The processor 710 may invoke instructions stored in the memory 720 tocontrol related operations, such as controlling the transmitter andreceiver to perform signal transmission and reception. According to anembodiment, the memory 720 stores instructions for the processor 710 tocontrol the following operations: dividing the closed place intomultiple areas including an entrance area, and having at least onecamera for shooting in any area, confirming a human being first enteringbased on a trajectory tracking of a human being in the entrance area,assigning a unique identity number to the human being, acquiring andrecording the feature information of the human being, and binding thefeature information with an identity number; and when re-identificationof the human being in the closed place is required, matching the featureinformation of the human being captured by the camera in an area wherethe human being is with the recorded feature information, and using theidentity number bound with the most matched feature information as theidentity number of the human being. The processor 710 may invokeinstructions stored in the memory 720 to control related operations. Itis easy to understand that the memory 720 may further store instructionsfor the processor 710 to control other operations according to theembodiments of the present disclosure, which will not be describedherein again.

From the above detailed description, those skilled in the art can easilyunderstand that the method, system and terminal device according to theembodiments of the present disclosure have one or more of the followingadvantages.

According to some exemplary embodiments of the present disclosure, byadopting a solution with combination of the multi-camera-based, thetrajectory tracking and feature matching, the problem of the human beingre-identification in real-time and in the complex scene is solved,achieving a better effect of the human being re-identification.

According to some exemplary embodiments of the present disclosure, bydisposing multiple cameras in the place to cover different areas anddifferent angles, any area can be shot by two or more cameras fromdifferent angles, which can solve the problem of occlusion between thehuman beings.

According to some exemplary embodiments of the present disclosure, byperforming association matching on all human beings in the entirescreen, associating and matching all human beings with the identitiescan help find global optimal matching, which improves the tolerance ofthe feature comparison greatly compared with the human beingre-identification in a single detection box.

Other embodiments of the present disclosure will be apparent to thoseskilled in the art from consideration of the specification and practiceof the present disclosure disclosed herein. The present disclosure isintended to cover any variations, uses, or adaptations of the presentdisclosure, which are in accordance with the general principles of thepresent disclosure and include common general knowledge or conventionaltechnical means in the art that are not disclosed in the presentdisclosure. The specification and embodiments are illustrative, and thereal scope and spirit of the present disclosure is defined by theappended claims.

It should be understood that the present disclosure is not limited tothe precise structures that have been described above and shown in thedrawings, and various modifications and changes can be made withoutdeparting from the scope thereof. The scope of the present disclosure islimited only by the appended claims.

What is claimed is:
 1. A method for re-identifying a human being in aclosed place, comprising: dividing the closed place into multiple areascomprising an entrance area, and having at least one camera for shootingin any area; confirming a human being first entering based on atrajectory tracking of the human being in the entrance area, assigningan identity number to the human being, acquiring and recording thefeature information of the human being, and binding the identity numberwith the feature information of the human being; and whenre-identification of a human being in the closed place is performed,matching the feature information of the human being captured by thecamera in an area where the human being is with the recorded featureinformation, and selecting the identity number bound with the recordedfeature information that best matches the feature information of thehuman being as the identity number of the human being.
 2. The methodaccording to claim 1, further comprising: when the human being leaves,deleting the feature information of the human being and marking thehuman being as having left.
 3. The method according to claim 1, whereinhaving at least one camera for shooting in any area comprises: havingtwo cameras with different shooting angles for shooting in any area. 4.The method according to claim 1, wherein confirming a human being firstentering based on a trajectory tracking of the human being in theentrance area comprises: judging whether the human being is the humanbeing first entering according to a trajectory of the human beingdisplayed on continuous shot screens with multiple frames of theentrance area.
 5. The method according to claim 1, wherein acquiringfeature information of the human being is performed by a convolutionalneural network technology.
 6. The method according to claim 1, whereinthe feature information of the human being comprises: stature, dress,and/or appearance.
 7. The method according to claim 1, wherein matchingthe feature information of the human being captured by the camera in anarea where the human being is with the recorded feature informationcomprises: performing matching on all human beings in the area.
 8. Themethod according to claim 1, wherein the feature information and theidentity number are stored in a human being feature database.
 9. Asystem for re-identifying a human being in a closed place, comprising: aprocessor; and a memory storing instructions for the processor tocontrol the following operations: dividing the closed place intomultiple areas comprising an entrance area, and having at least onecamera for shooting in any area; confirming a human being first enteringbased on a trajectory tracking of the human being in the entrance area,assigning an identity number to the human being, acquiring and recordingthe feature information of the human being, and binding the identitynumber with the feature information of the human being; and whenre-identification of a human being in the closed place is performed,matching the feature information of the human being captured by thecamera in an area where the human being is with the recorded featureinformation, and selecting the identity number bound with the recordedfeature information that best matches the feature information of thehuman being as the identity number of the human being.
 10. The systemaccording to claim 9, further comprising a feature database, configuredto store the feature information and the identity number.
 11. The systemaccording to claim 9, wherein the processor is further configured todelete the feature information of the human being and marking the humanbeing as having left, when the human being leaves.
 12. The systemaccording to claim 9, wherein having at least one camera for shooting inany area comprises: having two cameras with different shooting anglesfor shooting in any area.
 13. The system according to claim 9, whereinconfirming a human being first entering based on a trajectory trackingof the human being in the entrance area comprises: judging whether thehuman being is the human being first entering according to a trajectoryof the human being displayed on continuous shot screens with multipleframes of the entrance area.
 14. The system according to claim 9,wherein acquiring feature information of the human being is performed bya convolutional neural network technology.
 15. The system according toclaim 9, wherein the feature information of the human being comprises:stature, dress, and/or appearance.
 16. The system according to claim 9,wherein matching the feature information of the human being captured bythe camera in an area where the human being is with the recorded featureinformation comprises: performing matching on all human beings in thearea.
 17. The system according to claim 9, wherein the featureinformation and the identity number are stored in a human being featuredatabase.
 18. A non-transitory computer-readable storage medium having acomputer program stored thereon, wherein the program, when executed by aprocessor, causes to perform steps of the following method: dividing theclosed place into multiple areas comprising an entrance area, and havingat least one camera for shooting in any area; confirming a human beingfirst entering based on a trajectory tracking of the human being in theentrance area, assigning a unique identity number an identity number tothe human being, acquiring and recording the feature information of thehuman being, and binding the identity number with the featureinformation of the human being; and when re-identification of a humanbeing in the closed place is performed, matching the feature informationof the human being captured by the camera in an area where the humanbeing is with the recorded feature information, and selecting theidentity number bound with the recorded feature information that bestmatches the feature information of the human being as the identitynumber of the human being.